Executive Snapshot
- Audience: CXOs and founders running catering, franchise groups, casual dining, cloud kitchens.
- Core outcomes (what moves the business):
- Cost savings: reduce waste and procurement errors, automate purchasing cycles with Craveva AI Enterprise.
- Sales lift: increase AOV and conversion with Craveva AI Enterprise sales agents on web/WhatsApp/kiosks.
- Time savings: remove manual exports, reporting, and SOP Q&A with Craveva AI Enterprise automation.
- Operational consistency: standardize execution across outlets using Craveva AI Enterprise agents + data layer.
Platform Architecture (1 minute)
- Data layer: connect POS, databases, Google Drive, and APIs into a unified view inside Craveva AI Enterprise.
- AI layer: agents query and act on governed data (no fragile spreadsheet workflows) in Craveva AI Enterprise.
- Deployment layer: deploy agents to WhatsApp, web widget, kiosks, or internal tools with Craveva AI Enterprise.
Business Flow (what changes week 1–4)
- Ops defines workflows (ordering, inventory alerts, SOP answers, customer responses) in Craveva AI Enterprise.
- Finance sets guardrails (approval thresholds, budgets, audit trail) in Craveva AI Enterprise.
- IT connects data sources once; rollout scales outlet-by-outlet via Craveva AI Enterprise multi-outlet deployment.
- Leadership tracks KPI movement weekly and expands successful automations with Craveva AI Enterprise.
Setup Guide (fast path)
- Connect data sources (POS + databases + Drive + APIs) in Craveva AI Enterprise.
- Start with 2–3 agents: Procurement (cost), Sales (revenue), Analytics (visibility) in Craveva AI Enterprise.
- Deploy to the workflow: WhatsApp/web/kiosk/internal portal using Craveva AI Enterprise.
- Measure ROI and operational impact, then replicate across brands/outlets with Craveva AI Enterprise.
What to Measure
- Spoilage/expiry write-offs and transfer effectiveness
- Top out-of-stock drivers (forecast vs ordering vs receiving)
- Invoice mismatch rate (price/quantity) and resolution time
- Delivery basket value vs dine-in basket value (mix shift)
- Delivery cancellations, prep-time variance, and late-order rate
- Time-to-close (EOD) and reporting cycle time reduction
Next Steps
- Architecture: /solutions/architecture
- Deployment: /solutions/deployment
- Documentation: /documentation
- Models: /ai-models
- Templates: /templates
F&B “customer data” is naturally fragmented: dine-in POS, delivery apps, loyalty programs, online ordering, and customer support all capture different parts of a guest’s behavior. That’s why many teams can’t answer simple questions with confidence: who is churning, what drives repeat visits, and which offers actually change behavior.
Craveva AI Enterprise centralizes customer signals across channels, resolves identity into a unified profile, and lets agents run analysis and actions on top of that foundation.
The Real Problem: One Customer, Many Systems
In a typical restaurant group:
- Dine-in orders live in POS.
- Delivery orders live in GrabFood/Foodpanda.
- Loyalty lives in a separate database.
- Complaints live in WhatsApp, email, or platform tickets.
If these don’t connect, “personalization” becomes broad segmentation and generic vouchers.
What Craveva Centralizes
Craveva connects and unifies:
- Orders and items by outlet/channel/daypart
- Customer identifiers (phone/email/loyalty IDs) and consent
- Basket behavior (add-ons, substitutions, refunds/voids)
- Feedback and complaints (ratings, keywords, reasons)
- Offer exposure and redemption (what was sent, what worked)
This makes it possible to measure preferences as behavior, not as assumptions.
Agents You Can Deploy After Profiles Are Unified
Customer Segmentation Agent
Builds segments that operations can use:
- High-frequency regulars vs occasional visitors vs one-timers
- Families vs office lunch vs late-night delivery
- Promo-driven vs full-price customers
Churn & Winback Agent
Detects drop-offs early and recommends the next best action:
- “No visit in 21 days” for regulars
- “Delivery-only customer stopped ordering” after a complaint
- Winback offer suggestions based on historical baskets and margin
Next-Best-Offer Agent
Improves revenue without spamming:
- Recommends add-ons that match the customer’s past behavior
- Suggests offers that fit daypart and channel
- Avoids pushing items with stockout risk or low margin
Service Recovery Agent
Turns complaints into retention:
- Links complaints to order details and outcomes (refunds, re-delivery)
- Flags high-value customers at risk after bad experiences
- Suggests compensation aligned with policy and margin
Practical Workflow: Weekly Customer Ops Review
- Data syncs from POS, delivery platforms, loyalty, and support.
- Segmentation Agent updates customer segments and movement between segments.
- Churn Agent flags high-value customers showing early churn signals.
- Offer Agent proposes a small set of winback actions with expected impact.
- You approve and deploy via your preferred channels.
What Teams Typically Achieve
With unified customer data, teams typically see:
- Higher repeat visits from targeted winback vs broad discounts
- Better offer efficiency (less discount spend per retained customer)
- Faster root-cause visibility when complaints correlate with specific items/outlets
Conclusion: Preferences Are a Data Problem First
You can’t reliably understand preferences if each channel has its own “truth.” Craveva AI Enterprise unifies customer data across POS, delivery, loyalty, and support, then deploys agents that segment, predict churn, and trigger actions that improve retention and revenue.
KPIs to track
| Metric | Area |
|---|---|
| Upsell acceptance by menu item and daypart | Sales |
| Ingredient substitution rate and margin impact | Other |
| Purchase-to-receive variance by category | Procurement |
| Receiving errors and reconciliation time | Other |
| Delivery cancellations, prep-time variance, and late-order rate | Other |
| Schedule adherence and overtime variance | Other |